ObserverAgents
Observer Agents is an architectural pattern for multi-agent AI systems in which dedicated monitoring agents are added to watch other agents rather than participate in the task. An observer agent tracks the state of the system — whether agents are progressing, looping, going silent, or drifting from the original goal — and surfaces anomalies without intervening in the work itself.
Multi-agent systems introduce observability challenges that single-agent architectures do not have. When a pipeline of agents fails, the failure mode is often distributed: an agent may loop indefinitely, produce outputs that silently diverge from the task specification, or block downstream agents without emitting an error. Standard application monitoring does not capture these semantics.
The observer agent pattern addresses this by treating observability as a first-class architectural concern. Like a health-check sidecar in a microservices system, the observer agent runs alongside the working agents, monitors their behaviour against defined invariants, and reports anomalies to a supervisor or operator. It is stateless with respect to the task, which makes it easier to reason about and replace independently.
Placed in Assess at inner position because multi-agent systems are moving from prototype to production, and the absence of this pattern is a practical risk in complex pipelines. The inner position reflects that teams building production multi-agent systems should be actively evaluating it rather than adding observability as an afterthought. The pattern requires no specific tooling — it can be implemented with any agent framework that supports parallel agent execution.
